Covid-19 Project

Covid-19 Project

Investigation of the Data

In this project I used data associated with the Coronavirus pandemic from 2020. Here are three datasets named corona_confirmed.csv, corona_recovered.csv and corona_deaths.csv. For the entirety of this project, I’ll be using these datasets. You can find more recent versions of this data at Johns Hopkins’ data resource.

library(dplyr)
library(readr)
library(knitr)

# data loading
confirmed <- read_csv("corona_confirmed.csv")
deaths <- read_csv("corona_deaths.csv")
recovered <- read_csv("corona_recovered.csv")

# Inspection of the data
head(confirmed) %>% kable()
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20 1/31/20 2/1/20 2/2/20 2/3/20 2/4/20 2/5/20 2/6/20 2/7/20 2/8/20 2/9/20 2/10/20 2/11/20 2/12/20 2/13/20 2/14/20 2/15/20 2/16/20 2/17/20 2/18/20 2/19/20 2/20/20 2/21/20 2/22/20 2/23/20 2/24/20 2/25/20 2/26/20 2/27/20 2/28/20 2/29/20 3/1/20 3/2/20 3/3/20 3/4/20 3/5/20 3/6/20 3/7/20 3/8/20 3/9/20 3/10/20 3/11/20 3/12/20 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
NA Thailand 15.0000 101.0000 2 3 5 7 8 8 14 14 14 19 19 19 19 25 25 25 25 32 32 32 33 33 33 33 33 34 35 35 35 35 35 35 35 35 37 40 40 41 42 42 43 43 43 47 48 50 50 50 53 59 70 75 82 114 147 177 212 272 322 411 599
NA Japan 36.0000 138.0000 2 1 2 2 4 4 7 7 11 15 20 20 20 22 22 45 25 25 26 26 26 28 28 29 43 59 66 74 84 94 105 122 147 159 170 189 214 228 241 256 274 293 331 360 420 461 502 511 581 639 639 701 773 839 825 878 889 924 963 1007 1086
NA Singapore 1.2833 103.8333 0 1 3 3 4 5 7 7 10 13 16 18 18 24 28 28 30 33 40 45 47 50 58 67 72 75 77 81 84 84 85 85 89 89 91 93 93 93 102 106 108 110 110 117 130 138 150 150 160 178 178 200 212 226 243 266 313 345 385 432 455
NA Nepal 28.1667 84.2500 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
NA Malaysia 2.5000 112.5000 0 0 0 3 4 4 4 7 8 8 8 8 8 10 12 12 12 16 16 18 18 18 19 19 22 22 22 22 22 22 22 22 22 22 22 22 23 23 25 29 29 36 50 50 83 93 99 117 129 149 149 197 238 428 566 673 790 900 1030 1183 1306
British Columbia Canada 49.2827 -123.1207 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 2 4 4 4 4 4 4 4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 9 12 13 21 21 27 32 32 39 46 64 64 73 103 103 186 231 271 424 424
head(deaths) %>% kable()
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20 1/31/20 2/1/20 2/2/20 2/3/20 2/4/20 2/5/20 2/6/20 2/7/20 2/8/20 2/9/20 2/10/20 2/11/20 2/12/20 2/13/20 2/14/20 2/15/20 2/16/20 2/17/20 2/18/20 2/19/20 2/20/20 2/21/20 2/22/20 2/23/20 2/24/20 2/25/20 2/26/20 2/27/20 2/28/20 2/29/20 3/1/20 3/2/20 3/3/20 3/4/20 3/5/20 3/6/20 3/7/20 3/8/20 3/9/20 3/10/20 3/11/20 3/12/20 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
NA Thailand 15.0000 101.0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
NA Japan 36.0000 138.0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 2 4 4 5 6 6 6 6 6 6 6 6 10 10 15 16 19 22 22 27 29 29 29 33 35 40
NA Singapore 1.2833 103.8333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2
NA Nepal 28.1667 84.2500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
NA Malaysia 2.5000 112.5000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 3 4 10
British Columbia Canada 49.2827 -123.1207 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 4 4 7 7 8 10 10
head(recovered) %>% kable()
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20 1/31/20 2/1/20 2/2/20 2/3/20 2/4/20 2/5/20 2/6/20 2/7/20 2/8/20 2/9/20 2/10/20 2/11/20 2/12/20 2/13/20 2/14/20 2/15/20 2/16/20 2/17/20 2/18/20 2/19/20 2/20/20 2/21/20 2/22/20 2/23/20 2/24/20 2/25/20 2/26/20 2/27/20 2/28/20 2/29/20 3/1/20 3/2/20 3/3/20 3/4/20 3/5/20 3/6/20 3/7/20 3/8/20 3/9/20 3/10/20 3/11/20 3/12/20 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
NA Thailand 15.0000 101.0000 0 0 0 0 2 2 5 5 5 5 5 5 5 5 5 5 5 10 10 10 10 10 12 12 12 14 15 15 15 15 17 17 21 21 22 22 22 28 28 28 31 31 31 31 31 31 31 31 33 34 34 35 35 35 35 41 42 42 42 42 44
NA Japan 36.0000 138.0000 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 9 9 9 9 12 12 12 13 18 18 22 22 22 22 22 22 22 22 32 32 32 43 43 43 46 76 76 76 101 118 118 118 118 118 144 144 144 150 191 232 235
NA Singapore 1.2833 103.8333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 9 15 15 17 18 18 24 29 34 34 37 37 51 51 53 62 62 62 72 72 78 78 78 78 78 78 78 78 78 96 96 97 105 105 109 114 114 114 124 140 144
NA Nepal 28.1667 84.2500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
NA Malaysia 2.5000 112.5000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 3 3 3 3 7 7 7 13 15 15 15 15 15 18 18 18 18 18 18 18 18 22 22 22 22 23 24 24 24 26 26 26 35 42 42 49 60 75 87 114 139
British Columbia Canada 49.2827 -123.1207 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

Looking At March 22nd, 2020

The format of these three data frames are all the same; each row contains information about the number of cases in a certain province, state, or country. Every column (other than the columns containing the latitude, longitude, and country name) represents a date. We have data starting on January 22nd, 2020 and ending on March 22nd, 2020.

Let’s find the total number of confirmed cases on March 22nd, 2020.

# the total number of cases on March 22nd
count_confirmed <- confirmed %>%
                       select(`3/22/20`) %>%
                        sum()
count_confirmed
## [1] 335955

Filtering By Values

Let’s start to filter the data a bit. Three tasks are performed :

  • How many confirmed cases are there in countries on March 22nd that are north of the equator? (If a country is north of the equator, its latitude is greater than 0)
  • How many confirmed cases are there in March 22nd in Australia?
# countries in the northern hemisphere
northern_hemisphere_confirmed <- confirmed %>%
                            filter(Lat>0.0) %>%
                            select(`3/22/20`) %>%
                            sum()
northern_hemisphere_confirmed 
## [1] 329794
# percentage
northern_hemisphere_confirmed_percent <- (northern_hemisphere_confirmed/count_confirmed)*100
northern_hemisphere_confirmed_percent
## [1] 98.16612
# Filter for Australia cases
australia_cases <- confirmed %>%
                   filter(`Country/Region` == "Australia") %>%
                   select(`3/22/20`) %>%
                   sum()
australia_cases
## [1] 1314
#percentage
australia_cases_percent <- (australia_cases / count_confirmed)*100
australia_cases_percent
## [1] 0.3911238

Grouping By Country

Here some countries have multiple rows of data. This happens when a country has information about specific states or provinces. While this information might be useful, it makes it a bit tricky to see the total number of cases by country.

New data frame is created containing one row for every Country/Region. Every column of those new rows should have the sum of the total number of cases for that country for every day.

In this case every column other than Lat, Long, and Province/State is needed. summarize_at() only works with numbers.

After creating this new data frame inspection is done. To confirm correct calculation, I found the row for Australia and confirmed the number of cases on March 22nd. That matches my results from the previous step

# Group by countries
case_by_country <- confirmed %>%
                      group_by(`Country/Region`) %>%
                      summarize_at(vars(-Lat, -Long, -`Province/State`), sum)
head(case_by_country)  %>% kable()
Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20 1/31/20 2/1/20 2/2/20 2/3/20 2/4/20 2/5/20 2/6/20 2/7/20 2/8/20 2/9/20 2/10/20 2/11/20 2/12/20 2/13/20 2/14/20 2/15/20 2/16/20 2/17/20 2/18/20 2/19/20 2/20/20 2/21/20 2/22/20 2/23/20 2/24/20 2/25/20 2/26/20 2/27/20 2/28/20 2/29/20 3/1/20 3/2/20 3/3/20 3/4/20 3/5/20 3/6/20 3/7/20 3/8/20 3/9/20 3/10/20 3/11/20 3/12/20 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
Afghanistan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 5 7 7 7 11 16 21 22 22 22 24 24 40
Albania 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 10 12 23 33 38 42 51 55 59 64 70 76 89
Algeria 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 3 5 12 12 17 17 19 20 20 20 24 26 37 48 54 60 74 87 90 139 201
Andorra 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 39 39 53 75 88 113
Angola 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2
Antigua and Barbuda 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1
# Checking calculation confirmation by Australia count
australia_count <- case_by_country %>%
                   filter(`Country/Region` == "Australia") %>%
                   select(`3/22/20`) %>%
                   sum()
australia_count
## [1] 1314

Covid-19 ## Investigating The Recovered Dataset

Same process of grouping by country using the recovered dataset is done again. - What percentage of the cases in the US have recovered on March 22nd?

# Group by countries
recovered_by_country <- recovered %>%
                      group_by(`Country/Region`) %>%
                      summarize_at(vars(-Lat, -Long, -`Province/State`), sum)
US_recovered_22 <- recovered_by_country %>%
                    filter(`Country/Region` == "US") %>%
                    select(`3/22/20`)
                    sum()
## [1] 0
US_recovered_22
## # A tibble: 1 x 1
##   `3/22/20`
##       <dbl>
## 1         0

Its a surprising result; are there really zero recovered cases in the US? Let’s take a closer look at the US row in the recovered table.

# Filtering to inspect the US row
recovered_by_country %>%
                    filter(`Country/Region` == "US")  %>% kable()
Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20 1/31/20 2/1/20 2/2/20 2/3/20 2/4/20 2/5/20 2/6/20 2/7/20 2/8/20 2/9/20 2/10/20 2/11/20 2/12/20 2/13/20 2/14/20 2/15/20 2/16/20 2/17/20 2/18/20 2/19/20 2/20/20 2/21/20 2/22/20 2/23/20 2/24/20 2/25/20 2/26/20 2/27/20 2/28/20 2/29/20 3/1/20 3/2/20 3/3/20 3/4/20 3/5/20 3/6/20 3/7/20 3/8/20 3/9/20 3/10/20 3/11/20 3/12/20 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
US 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5 5 6 6 6 7 7 7 7 7 7 7 7 7 7 7 8 8 12 12 12 12 17 17 0 0 0 0 0

It seems like the number of recovered cases is steadily increasing to 17, until March 18th, when it suddenly drops back to 0. This is surprising, and not what is expected!

When I went back to Johns Hopkins’ resource we found a note saying that the data had moved into a different file.

We could report the maximum number of confirmed and recovered cases.

# Finding the maximum number of confirmed and recovered cases
max_recovered_US <- recovered_by_country %>%
                    filter(`Country/Region` == "US") %>%
                     select(-`Country/Region`) %>%
                    max()
max_recovered_US
## [1] 17
max_case_US <- case_by_country %>%
                    filter(`Country/Region` == "US") %>%
                     select(-`Country/Region`) %>%
                    max()
max_case_US
## [1] 33272

Transposing Data Frames

By transposing we could then find the maximum value of a country by simply selecting the appropriate column and finding the maximum value in that column. Let’s try that!

# Transposing the data frame
case_by_country <- case_by_country %>% 
                       t() %>%
                        as.data.frame()
head(case_by_country) %>% kable()
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38 V39 V40 V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55 V56 V57 V58 V59 V60 V61 V62 V63 V64 V65 V66 V67 V68 V69 V70 V71 V72 V73 V74 V75 V76 V77 V78 V79 V80 V81 V82 V83 V84 V85 V86 V87 V88 V89 V90 V91 V92 V93 V94 V95 V96 V97 V98 V99 V100 V101 V102 V103 V104 V105 V106 V107 V108 V109 V110 V111 V112 V113 V114 V115 V116 V117 V118 V119 V120 V121 V122 V123 V124 V125 V126 V127 V128 V129 V130 V131 V132 V133 V134 V135 V136 V137 V138 V139 V140 V141 V142 V143 V144 V145 V146 V147 V148 V149 V150 V151 V152 V153 V154 V155 V156 V157 V158 V159 V160 V161 V162 V163 V164 V165 V166 V167 V168 V169 V170 V171
Country/Region Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Benin Bhutan Bolivia Bosnia and Herzegovina Brazil Brunei Bulgaria Burkina Faso Cabo Verde Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Colombia Congo (Brazzaville) Congo (Kinshasa) Costa Rica Cote d’Ivoire Croatia Cruise Ship Cuba Cyprus Czechia Denmark Djibouti Dominica Dominican Republic East Timor Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Eswatini Ethiopia Fiji Finland France Gabon Gambia, The Georgia Germany Ghana Greece Grenada Guatemala Guinea Guyana Haiti Holy See Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, South Kosovo Kuwait Kyrgyzstan Latvia Lebanon Liberia Liechtenstein Lithuania Luxembourg Madagascar Malaysia Maldives Malta Martinique Mauritania Mauritius Mexico Moldova Monaco Mongolia Montenegro Morocco Mozambique Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria North Macedonia Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Saint Lucia Saint Vincent and the Grenadines San Marino Saudi Arabia Senegal Serbia Seychelles Singapore Slovakia Slovenia Somalia South Africa Spain Sri Lanka Sudan Suriname Sweden Switzerland Syria Taiwan* Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Arab Emirates United Kingdom Uruguay US Uzbekistan Venezuela Vietnam Zambia Zimbabwe
1/22/20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 548 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
1/23/20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 643 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0
1/24/20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 920 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 3 0 5 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0
1/25/20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1406 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 3 0 7 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0
1/26/20 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2075 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 3 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 4 0 8 0 0 0 0 0 0 0 0 0 0 5 0 0 2 0 0

Column Name Set

library(janitor)

# Make the first row the column titles
case_by_country <- case_by_country %>%
                          row_to_names(row_number = 1 )
head(case_by_country,26)  %>% kable()
Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Benin Bhutan Bolivia Bosnia and Herzegovina Brazil Brunei Bulgaria Burkina Faso Cabo Verde Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Colombia Congo (Brazzaville) Congo (Kinshasa) Costa Rica Cote d’Ivoire Croatia Cruise Ship Cuba Cyprus Czechia Denmark Djibouti Dominica Dominican Republic East Timor Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Eswatini Ethiopia Fiji Finland France Gabon Gambia, The Georgia Germany Ghana Greece Grenada Guatemala Guinea Guyana Haiti Holy See Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, South Kosovo Kuwait Kyrgyzstan Latvia Lebanon Liberia Liechtenstein Lithuania Luxembourg Madagascar Malaysia Maldives Malta Martinique Mauritania Mauritius Mexico Moldova Monaco Mongolia Montenegro Morocco Mozambique Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria North Macedonia Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Saint Lucia Saint Vincent and the Grenadines San Marino Saudi Arabia Senegal Serbia Seychelles Singapore Slovakia Slovenia Somalia South Africa Spain Sri Lanka Sudan Suriname Sweden Switzerland Syria Taiwan* Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Arab Emirates United Kingdom Uruguay US Uzbekistan Venezuela Vietnam Zambia Zimbabwe
1/22/20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 548 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
1/23/20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 643 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0
1/24/20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 920 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 3 0 5 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0
1/25/20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1406 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 3 0 7 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0
1/26/20 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2075 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 3 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 4 0 8 0 0 0 0 0 0 0 0 0 0 5 0 0 2 0 0
1/27/20 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 2877 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 4 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 1 0 0 0 0 0 5 0 8 0 0 0 0 0 0 0 0 0 0 5 0 0 2 0 0
1/28/20 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 5509 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 4 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 1 0 0 0 0 0 8 0 14 0 0 0 0 0 0 0 0 0 0 5 0 0 2 0 0
1/29/20 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 6087 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 5 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 4 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 1 0 0 0 0 0 8 0 14 0 0 0 0 0 0 0 4 0 0 5 0 0 2 0 0
1/30/20 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 8141 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 5 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 11 0 0 0 4 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 1 0 0 0 0 0 9 0 14 0 0 0 0 0 0 0 4 0 0 5 0 0 2 0 0
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2/1/20 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 4 0 0 0 0 11891 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 6 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 20 0 0 0 12 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 0 0 0 0 0 0 0 16 0 0 0 0 1 1 0 0 1 0 0 10 0 19 0 0 0 0 0 0 0 4 2 0 8 0 0 6 0 0
2/2/20 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 4 0 0 0 0 16630 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 6 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 2 0 20 0 0 0 15 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 18 0 0 0 0 1 1 0 0 1 0 0 10 0 19 0 0 0 0 0 0 0 5 2 0 8 0 0 6 0 0
2/3/20 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 4 0 0 0 0 19716 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 6 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 2 0 20 0 0 0 15 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 18 0 0 0 0 1 1 0 0 1 0 0 10 0 19 0 0 0 0 0 0 0 5 2 0 11 0 0 8 0 0
2/4/20 0 0 0 0 0 0 0 0 13 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 4 0 0 0 0 23707 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 6 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 2 0 22 0 0 0 16 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 24 0 0 0 0 1 1 0 0 1 0 0 11 0 25 0 0 0 0 0 0 0 5 2 0 11 0 0 8 0 0
2/5/20 0 0 0 0 0 0 0 0 13 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 5 0 0 0 0 27440 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 6 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 2 0 22 0 0 0 19 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 28 0 0 0 0 1 1 0 0 1 0 0 11 0 25 0 0 0 0 0 0 0 5 2 0 11 0 0 8 0 0
2/6/20 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 5 0 0 0 0 30587 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 6 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 2 0 45 0 0 0 23 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 28 0 0 0 0 1 1 0 0 1 0 0 16 0 25 0 0 0 0 0 0 0 5 2 0 11 0 0 10 0 0
2/7/20 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 7 0 0 0 0 34110 0 0 0 0 0 0 61 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 6 0 0 0 13 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 0 25 0 0 0 24 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0 30 0 0 0 0 1 1 0 0 1 0 0 16 0 25 0 0 0 0 0 0 0 5 3 0 11 0 0 10 0 0
2/8/20 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 7 0 0 0 0 36814 0 0 0 0 0 0 61 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 11 0 0 0 13 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 0 25 0 0 0 24 0 0 0 0 0 0 0 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0 33 0 0 0 0 1 1 0 0 1 0 0 17 0 32 0 0 0 0 0 0 0 7 3 0 11 0 0 13 0 0
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2/10/20 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 7 0 0 0 0 42354 0 0 0 0 0 0 135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 11 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 0 26 0 0 0 27 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0 45 0 0 0 0 2 1 0 0 1 0 0 18 0 32 0 0 0 0 0 0 0 8 8 0 11 0 0 14 0 0
2/11/20 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 7 0 0 0 0 44386 0 0 0 0 0 0 135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 11 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 0 26 0 0 0 28 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0 47 0 0 0 0 2 1 0 0 1 0 0 18 0 33 0 0 0 0 0 0 0 8 8 0 12 0 0 15 0 0
2/12/20 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 7 0 0 0 0 44759 0 0 0 0 0 0 175 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 11 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 0 28 0 0 0 28 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0 50 0 0 0 0 2 1 0 0 1 0 0 18 0 33 0 0 0 0 0 0 0 8 9 0 12 0 0 15 0 0
2/13/20 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 7 0 0 0 0 59895 0 0 0 0 0 0 175 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 11 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 0 28 0 0 0 28 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0 58 0 0 0 0 2 1 0 0 1 0 0 18 0 33 0 0 0 0 0 0 0 8 9 0 13 0 0 16 0 0
2/14/20 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 7 0 0 0 0 66358 0 0 0 0 0 0 218 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 11 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 0 29 0 0 0 28 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0 67 0 0 0 0 2 1 0 0 1 0 0 18 0 33 0 0 0 0 0 0 0 8 9 0 13 0 0 16 0 0
2/15/20 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 7 0 0 0 0 68413 0 0 0 0 0 0 285 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 12 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 0 43 0 0 0 28 0 0 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0 72 0 0 0 0 2 1 0 0 1 0 0 18 0 33 0 0 0 0 0 0 0 8 9 0 13 0 0 16 0 0
2/16/20 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 7 0 0 0 0 70513 0 0 0 0 0 0 355 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 12 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 0 59 0 0 0 29 0 0 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0 75 0 0 0 0 2 1 0 0 1 0 0 20 0 34 0 0 0 0 0 0 0 9 9 0 13 0 0 16 0 0

I printed the head of the data frame thats been just created. The columns are now of type <fctr>, or factor. This was one of the side effects of rotating the data frame.I turned all of these columns back into doubles.

# Transforming the columns to numeric values
case_by_country <- case_by_country %>%
                           apply(MARGIN = 2, as.numeric) %>%
                           as.data.frame()
head(case_by_country,25)  %>% kable()
Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Benin Bhutan Bolivia Bosnia and Herzegovina Brazil Brunei Bulgaria Burkina Faso Cabo Verde Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Colombia Congo (Brazzaville) Congo (Kinshasa) Costa Rica Cote d’Ivoire Croatia Cruise Ship Cuba Cyprus Czechia Denmark Djibouti Dominica Dominican Republic East Timor Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Eswatini Ethiopia Fiji Finland France Gabon Gambia, The Georgia Germany Ghana Greece Grenada Guatemala Guinea Guyana Haiti Holy See Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, South Kosovo Kuwait Kyrgyzstan Latvia Lebanon Liberia Liechtenstein Lithuania Luxembourg Madagascar Malaysia Maldives Malta Martinique Mauritania Mauritius Mexico Moldova Monaco Mongolia Montenegro Morocco Mozambique Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria North Macedonia Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Saint Lucia Saint Vincent and the Grenadines San Marino Saudi Arabia Senegal Serbia Seychelles Singapore Slovakia Slovenia Somalia South Africa Spain Sri Lanka Sudan Suriname Sweden Switzerland Syria Taiwan* Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Arab Emirates United Kingdom Uruguay US Uzbekistan Venezuela Vietnam Zambia Zimbabwe
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Sars CoV-2

Sars CoV-2

Let’s once again find the maximum number of cases reported in the US.

# Find the maximum number of confirmed cases in the US
case_by_country %>%
  select(US) %>%
  max()
## [1] 33272

Visualization

I’ve build a line graph showing the number of confirmed cases over time for a particular country. To do this, first I need to add a new column to dataset to represent the date. The first day in our dataset was January 22nd. Let’s represent that as day 1. January 23rd would then be day 2, and so on.

# Adding the date column
nrow(case_by_country)
## [1] 61
case_by_country <- case_by_country %>%
                        mutate(date = 1:61)
head(case_by_country, 28) %>% kable()
Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Benin Bhutan Bolivia Bosnia and Herzegovina Brazil Brunei Bulgaria Burkina Faso Cabo Verde Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Colombia Congo (Brazzaville) Congo (Kinshasa) Costa Rica Cote d’Ivoire Croatia Cruise Ship Cuba Cyprus Czechia Denmark Djibouti Dominica Dominican Republic East Timor Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Eswatini Ethiopia Fiji Finland France Gabon Gambia, The Georgia Germany Ghana Greece Grenada Guatemala Guinea Guyana Haiti Holy See Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, South Kosovo Kuwait Kyrgyzstan Latvia Lebanon Liberia Liechtenstein Lithuania Luxembourg Madagascar Malaysia Maldives Malta Martinique Mauritania Mauritius Mexico Moldova Monaco Mongolia Montenegro Morocco Mozambique Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria North Macedonia Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Saint Lucia Saint Vincent and the Grenadines San Marino Saudi Arabia Senegal Serbia Seychelles Singapore Slovakia Slovenia Somalia South Africa Spain Sri Lanka Sudan Suriname Sweden Switzerland Syria Taiwan* Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Arab Emirates United Kingdom Uruguay US Uzbekistan Venezuela Vietnam Zambia Zimbabwe date
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Let’s now see the number of cases in Bangladesh over the days in the dataset.

Let’s now see the number of cases in Bangladesh over the days in the dataset.

library(ggplot2)
# Creating a line graph with date on the X axis and number of cases in Bangladesh on the Y axis
case_by_country %>% ggplot(aes(x = date, y = Bangladesh)) +
                      geom_line()

That line of code is pretty concise. Having a column containing only the confirmed cases from a particular country made this graph relatively simple to create.

Finally, I did a bit of work to add title.

# Adding a proper title, x label, and y label
case_by_country %>% ggplot(aes(x = date, y = Bangladesh, color = Bangladesh )) +
                      geom_line() +
                      labs(x = "Number of days since January 22nd, 2020", y = "Number of confirmed cases", title = "Covid-19 Confirmed cases In Bangladesh")


  1. Author github: Emon-ProCoder7 2. Author Linked-in:Md Tabassum Hossain Emon